Method and apparatus for utilizing user feedback to improve signifier mapping

ABSTRACT

An apparatus for finding resources on a network comprises a finder server having access to: (a) a database including: (i) an index of resources available on network of interconnected computers on which a plurality of resources reside; and (ii) information regarding user feedback gathered from previous operations of the apparatus by a user and plural previous users; and (b) a learning system operable to access and learn from information contained on the database. The finder server is operable to locate, in response to entry by the user of a resource identity signifier, a single intended target resource intended by the user to uniquely correspond to the resource identity signifier, from among a plurality of resources located on the network, by: receiving a resource identity signifier from the user; accessing the database to determine, based on the information in the database, which, if any, of the indexed resources is likely to be the intended target resource; and directing a computer of the user so as to cause that computer to connect the user to the address of the resource, if any, determined as likely to be the intended target resource.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to a computer-implemented product forlocating and connecting to a particular desired object or targetresource from among plural resources resident at distributed locationson a network.

2. Description of the Related Art

The worldwide network of computers known as the Internet evolved frommilitary and educational networks developed in the late 1960's. Publicinterest in the Internet has increased of late due to the development ofthe World Wide Web (hereinafter, the Web), a subset of the Internet thatincludes all connected servers offering access to hypertext transferprotocol (HTTP) space. To navigate the Web, browsers have been developedthat give a user the ability to download files from Web pages, datafiles on server electronic systems, written in HyperText Mark-UpLanguage (HTML). Web pages may be located on the Web by means of theirelectronic addresses, known as Uniform Resource Locators (URLs).

A URL uniquely identifies the location of a resource (web page) withinthe Web. Each URL consists of a string of characters defining the typeof protocol needed to access the resource (e.g., HTTP), a network domainidentifier, identification of the particular computer on which theresource is located, and directory path information within thecomputer's file structure. The domain name is assigned by NetworkSolutions Registration Services after completion of a registrationprocess.

While the amount of information available on the Web is enormous, andtherefore potentially of great value, the sheer size of the Web makesthe search for information, and particular web sites or pages, adaunting task. Search engines have been developed to assist personsusing the Web in searching for web pages that may contain usefulinformation.

Search engines fall into two major categories. In search engines fallinginto the first category, a service provider compiles a directory of Websites that the provider's editors believe would be of interest to usersof the service. The Yahoo site is the best known example of such aprovider. Products in this category are not, strictly speaking, searchengines, but directories, and will be referred to hereinafter as“editor-controlled directories”. In an editor-controlled directory, thedeveloper of the directory (the “editor”) determines, based upon what itbelieves users want, what search terms map to what web pages.

The other major category, exemplified by Altavista, Lycos, and Hotbot,uses search programs, called “web crawlers”, “web spiders”, or “robots”,to actively search the Web for pages to be indexed, which are thenretrieved and scanned to build indexes. Most commonly, this is done byprocessing the full text of the page and extracting words, phrases, andrelated descriptors (word adjacencies, frequencies, etc.). This is oftensupplemented by examining descriptive information about the Web documentcontained in a tag or tags in the header of a page. Such tags are knownas “metatags” and the descriptive information contained therein as“metadata”. These products will be referred to hereinafter as“author-controlled search engines,” since the authors of the Webdocuments themselves control, to some extent, whether or not a searchwill find their document, based upon the metadata that the authorincludes in the document.

Each type of product has its disadvantages. Author-controlled searchengines tend to produce search results of enormous size. However, theyhave not been reliable in reducing the large body of information to amanageable set of relevant results. Further, web site authors oftenattempt to skew their site's position in the search results ofauthor-controlled search engines by loading their web site metatags withmultiple occurrences of certain words commonly used in searches.

Editor-controlled directories are more selective in this regard.However, because conventional editor-controlled directories do notactively search the web for matches to particular search terms, they maymiss highly relevant web sites that were not deemed by the editors to beworthy of inclusion in the directory. Also, it is possible for theeditor to “play favorites” among the multitude of Web documents bymapping certain Web documents to more search terms than others.

Recently, search engines such as DirectHit (www.directhit.com) haveintroduced feedback and learning techniques to increase the relevancy ofsearch results. DirectHit purports to use feedback to iteratively modifysearch result rankings based on which search result links are actuallyaccessed by users. Another factor purportedly used in the DirectHitservice in weighting the results is the amount of time the user spendsat the linked site. The theory behind such techniques is that, ingeneral, the more people that link on a search result, and the longerthe amount of time they spend there, the greater the likelihood thatusers have found this particular site relevant to the entered searchterms. Accordingly, such popular sites are weighted and appear higher insubsequent result lists for the same search terms. The Lycos searchengine (www.lycos.com) also uses feedback, but only at the time ofcrawling, not in ranking of results. In the Lycos search engine, asdescribed in U.S. Pat. No. 5,748,954, priority of crawling is set basedupon how many times a listed web site is linked to from other web sites.This idea of using information on links to a page was later exploited bythe Clever system developed in research by IBM, and the Google system(www.google.com), which do use such information to rank possible hitsfor a search query.

Even leaving aside the drawbacks discussed above, search engines of bothcategories are most useful when a user desires a list of relevant websites for particular search terms. Often, users wish to locate aparticular web site but do not know the exact URL of the desired website. Conventional search engines are not the most efficient tools fordoing this.

Moreover, naming and locating particular sites on the Web is currentlysubject to serious problems. For example, appropriate names, includingexisting company names or trademarks, may not be available, becausesomeone registered them first. Names may be awkward and not obvious,because of length, form/coding difficulties or variant forms, and namesmay not justify a separate domain name registration for reasons of costand convenience, such as movie titles or individual products.

This problem results from a mismatch between the present networkaddressing scheme based on Uniform Resource Locators (URLs), which meetthe technical needs of the Internet software, and the needs of humanusers and site sponsors for simple, user-friendly mnemonic and brandednames. This problem is largely hidden in cases where a user finds a siteby clicking a pre-coded link (such as after using a search engine), orby using a saved bookmark. However, the problem does seriously affectusers wishing to find a site directly, or to tell another person how tofind it. To do this, the person must know and type the URL into hisInternet browser, typically of the form sitename.com orwww.sitename.com. Site sponsors are also seriously hampered by thisdifficulty in publicizing their sites.

Further, the current method of naming and locating Web sites hasserious, widely known problems. Web site locator “domain” names areoften not simple or easily remembered or guessed, and often do notcorrespond to company, trademark, brand or other well-known names.

As a result of the foregoing, site URLS (or domain names) are notintuitively obvious in most cases, and incorrect access attempts wastetime and produce cryptic error messages that provide no clue as to whatthe correct URL might be. A significant percentage of searches are forspecific, well-known sites. These could be found much more quickly by aspecial-purpose locator engine. The current mode of interacting withsearch engines is also cumbersome-for this purpose, a much simplifiedmode of direct entry is practical.

One attempt to provide the ability to map a signifier, or alias, to aspecific URL utilizes registration of key words, or aliases, which whenentered at a specified search engine, will associate the entered keyword with the URL of the registered site. One such commercialimplementation of this technique is known as NetWord (www.netword.com).However, the NetWord aliases are assigned on a registration basis, thatis, owners of web sites pay NetWord a registration fee to be mapped toby a particular key word. As a result, the URL returned by NetWord mayhave little or no relation to what a user actually would be looking for.Another key word system, RealNames (www.realnames.com), similarly allowsweb site owners to register, for a fee, one or more “RealNames” that canbe typed into browser incorporating RealNames' software, in lieu of aURL. Since RealNames also is registration based, there is no guaranteethat the URL to which is user is directed will be the one he intended.

Further, in existing preference learning and rating mechanisms, such ascollaborative filtering (CF) and relevance feedback (RF), the objectiveis to evaluate and rank the appeal of the best n out of m sites or pagesor documents, where none of the n options are necessarily known to theuser in advance, and no specific one is presumed to be intended. It is amatter of interest in any suitable hit, not intent for a specifictarget. Results may be evaluated in terms of precision (whether “poor”matches are included) and recall (whether “good” matches are notincluded).

A search for “IBM” may be for the IBM Web site, but it could just aslikely be for articles about IBM as a company, or articles withinformation on IBM-compatible PCs, etc. Typical searches are forinformation about the search term, and can be satisfied by any number of“relevant” items, any or all of which may be previously unknown to thesearcher. In this sense there is no specific target object (page,document, record, etc.), only some open ended set of objects which maybe useful with regard to the search term. The discovery search term doesnot signify a single intended object, but specifies a term (which is anattribute associated with one or more objects) presumed to lead to anynumber of relevant items. Expert searchers may use searches that specifythe subject indirectly, to avoid spurious hits that happen to contain amore direct term. For example, searching for information about the bookGone With The Wind may be better done by searching for MargaretMitchell, because the title will return too many irrelevant hits thatare not about the book itself (but may be desired for some other task).

In other words, the general case of discovery searching that typicalsearch engines are tuned to serve is one where a search is desired toreturn some number, n, of objects, all of which are relevant. A keyperformance metric, recall, is the completeness of the set of resultsreturned. The case of a signifier for an object, is the special case ofn=1. only one specific item is sought. Items that are not intended arenot desired—their relevance is zero, no matter how good or interestingthey may be in another context. The top DirectHit for “Clinton” was aMonica Lewinsky page. That is probably not because people searching forClinton actually intended to get that page, but because of serendipityand temptation—which is a distraction, if what we want is to find theWhite House Web site.

In addition,

-   -   CF obtains feedback from a group of users in order to serve each        given user on an overall, non-contingent basis—without regard to        the either the intent of the user at a specific time, or to        being requested in a specific context.    -   RF is used by a single user to provide feedback on their intent        at a given time, but still with no presumed intent of a single        target.

More broadly, searching techniques are generally not optimized based onusing a descriptor which is also an identifier—they provide moregenerally for the descriptor to specify the nature of the content of thetarget, not its name. There are options in advanced search techniqueswhich allow specification that the descriptor is actually an identifier,such as for searching by title. Such options may be used to constrainthe search when a specific target happens to be intended, but no specialprovision is made to apply feedback to exploit that particularrelationship or its singularity.

Moreover, none of the currently available key word systems utilizeheuristic techniques actually to determine the site intended by theuser. Instead, the current systems teach away from such an approach bytheir use of registration, rather than user intention, to assign keywords to map to web pages. Thus, the current techniques are not directedto solving the problem of finding the one, correct site for a particularsignifier.

Thus, the need exists for a system that would enable a user to find adesired Web document by simply entering an intuitive key word or aliasand that would perform a one to one mapping of the alias with the URLactually desired by the user, and which would use heuristic techniquesto assist in providing the correct mapping, and improving systemaccuracy over time.

SUMMARY OF THE INVENTION

In consideration of the above deficiencies of the prior art, it is anobject of the present invention to provide a method of signifier mappingthat allows a user to locate to a particular network resource, in thepreferred embodiment a web page, by simply entering a signifier oralias.

Thus, the present invention is generally directed to a technique forintelligent searching or matching where a signifier is given and is tobe related to a name or address of an intended target object.

Signifier, in the context of the present invention means:

-   -   an identifier, referent, or synonym for the name or address of a        specific resource (a target object) presumed to exist in some        domain; but    -   not necessarily a “name” or “address”—a canonical identifier        that has been assigned by some authority or pre-set by some        convention (names are a subset of signifiers—those which are        canonical or pre-established);    -   not necessarily a description of content or subject matter        (concepts or words);    -   an identifier that has cognitive significance to the user, and        presumed communication value in identifying the intended target        object to another person or intelligent agent.

In addition, this cognitive/communication value is based on a perceivedrelationship (meant to have minimal ambiguity) to an identifier, whichmight be an assigned name or a name based on common usage, but whichneed not be exact, as long at it serves to signify the intended target.

More generally, descriptors may possibly be considered to be signifiers,if they are intended to be unique or minimally ambiguous (e.g. “thecompany that commercialized Mosaic” or “the company that sells theThinkPad”).

It is a further object of the present invention to provide a system inwhich heuristic techniques are used together with user feedback toimprove the accuracy of signifier mapping.

None of the many solutions to the signifier mapping problem (Netword,Centraal, Goto, etc.) have identified learning as a valuable technique.This may be because what naturally come to mind are techniques based onpre-defined mappings that make the use of “de jure” explicitregistration. That teaches away from the idea of trying to learn themappings heuristically from colloquial usage. (The same applies toattempts at creating systems for “user friendly names” in otherdirectory systems.) Since the mappings are understood as being definedor registered, why would one try to learn about them? But actually, themappings are just like natural language-they are dynamic, evolving, andambiguous, and can only be resolved in terms of learned usage within acontext—which is best addressed by learning, as in the presentinvention, not registration or other static mappings as appear in theprior art.

The use of heuristic, adaptive feedback-based techniques operates insignificantly different ways when focused on signifier mapping, and thiscan be exploited by isolating such tasks. A key difference between thepresent invention and most common searching tasks is that in the priorsearching techniques, there is no intention of a specific target objectthat is known to exist.

The present invention has several advantageous features, variouscombinations of which are possible:

-   1) a special purpose mapping engine for locating popular sites by    guessed names;-   2) automatic display of the target site (if located with reasonable    confidence);-   3) an optional simplified mode of direct entry of a guessed site    name; and-   4) use of user expectations, such as popularity of guesses intended    for a given site, as a primary criterion for translating names to    sites, with provision for protection of registered trademarks or    other mandates.

In accordance with one aspect of the present invention, a finder orlocator server is established. The server is configured to work with auser interface that allows users to enter an guessed name or alias, aseasily as if the user knew the correct URL for the intended targetresource. In response to entry of the alias, the finder server accessesa database that includes, in a preferred embodiment, conventionalWeb-crawler-derived index information, domain name registrationinformation, as well as user feedback from previous users of the server,and looks up the correct URL, i.e., the one URL that corresponds to thealias and causes the user's browser to go automatically to that URL,without the user having to view and click on a search results page, ifthe correct URL can be determined with a predetermined degree ofconfidence.

In one preferred embodiment, the server is structured to accept thealias as a search argument and do a lookup of the correct URL and thereturn of same to the browser, without the intermediate step of the userhaving to wait for and then click on a search results web page. Theautomatic transfer is preferably effected using standard HTMLfacilities, such as a redirect page or framing. Redirect is effected byplacing pre-set redirection pages at the guess URL on the server.Alternately, the redirect page can be generated dynamically by programlogic on the server that composes the page when requested.

The present invention advantageously uses feedback and heuristictechniques to improve the accuracy of the determination of the correctURL. If a suggested match is found by the look-up technique and theaccuracy of the mapping is confirmed by user feedback, then, afterdirecting the user to the URL, the result is stored in the server toimprove the accuracy of subsequent queries. The server database includesa list of expected terms and expected variants that can initially becatalogued to provide for exact matches. This list is updated by thelearning processes discussed in more detail below.

If, on the other hand, a probable one intended match cannot bedetermined, the finder server preferably uses intelligent techniques tofind a selection of links to possible matches ranked in order oflikelihood, or could return a no-match page with advice, or aconventional search interface or further directories.

According to a preferred embodiment of the invention, each of theselection of links are configured not to go directly to the target URL.Rather, the links are directed back to a redirect server established bythe finder server, with coding that specifies the true target, andfeedback information. The finder server can in this way keep track ofuser selections.

In accordance with an advantageous aspect of the invention, suchfeedback information is used to improve the results of the search bypromoting web sites almost universally selected to exact match status,and by improving the ranking of possible lists in accordance with whichlinks are most often selected. Preferably, a confidence parameter can begenerated from such tracking to control whether to redirect to a URL orto present a possible list to users.

In furtherance of the above and other objects, there is provided, adesignated server, accessible on the Internet, the designated serverbeing configured to respond to relocation requests that specify anidentifier, corresponding to a target resource, that may not be directlyresolvable by standard Internet Protocol name resolution services to theURL of the target resource. In a direct entry embodiment of the presentinvention, requests are passed to the relocation server by sending arelocation URL that designates the relocation server as the destinationnode and appends the identifying information for the identifier as partof a URL string. The relocation server extracts the identifyinginformation and translates it into a valid URL for the target resource.The relocation server is configured, in the event that a unique URL canbe determined with respect to the target resource, to cause the targetresource to be presented to the user without further action on the partof the user.

Preferably, the user requests are entered at a web browser, therelocation or search server determines the valid URL for the targetresource by performing a look-up in a database, and the response fromthe relocation server is in the form of a redirect page that causes theuser's web browser to obtain the target resource.

In accordance with one aspect of the present invention, there isprovided a method of finding, in response to entry by a user of aresource identity signifier, a single intended target resource intendedby the user to uniquely correspond to the resource identity signifier,among a plurality of resources located on a network comprising aplurality of interconnected computers. The method is for use on a finderserver having access to: (a) a database including (i) an index ofresources available on the network; and (ii) information regarding userfeedback gathered in previous executions of the method by the user andplural previous users; and (b) a learning system structured to accessand learn from information contained in the database. The methodcomprises: receiving a resource identity signifier from the user; andaccessing the database to determine, based on the information in thedatabase, which, if any, of the indexed resources is likely to be theintended target resource. Preferably, the method further comprisesdirecting a computer of the user so as to enable that computer toconnect the user to the address of the resource, if any, determined aslikely to be the intended target resource.

In accordance with another aspect of the present invention, there isprovided an apparatus comprising a finder server having access to: (a) adatabase including: (i) an index of resources available on network ofinterconnected computers on which a plurality of resources reside; and(ii) information regarding user feedback gathered in previous operationsof the apparatus by a user and plural previous users; and (b) a learningsystem operable to access and learn from information contained in thedatabase. The finder server is operable to locate, in response to entryby the user of a resource identity signifier, a single intended targetresource intended by the user to uniquely correspond to the resourceidentity signifier, from among a plurality of resources located on thenetwork, by: receiving a resource identity signifier from the user; andaccessing the database to determine, based on the information in thedatabase, which, if any, of the indexed resources is likely to be theintended target resource. Preferably, a computer of the user is directedso as to cause that computer to connect the user to the address of theresource, if any, determined to be the intended target resource.

In accordance with yet another aspect of the present invention, there isprovided a system for finding, in response to entry by a user of aresource identity signifier, a single intended target resource intendedby the user to uniquely correspond to the resource identity signifier,among a plurality of resources located on a network comprising aplurality of interconnected computers. The system comprises: finderserver means having access to: (a) database means for storing an indexof resources available on the network; and information regarding userfeedback gathered in previous executions of the system by the user andplural previous users; and (b) learning system means for accessing andlearning from information contained on the database; receiving means forreceiving a resource identity signifier from the user; and accessingmeans for accessing the database means to determine which, if any, ofthe indexed resources is likely to be the desired target resource.Preferably, the system further comprises directing means for directing acomputer of the user so as to cause that computer to connect the user tothe address of the resource, if any, determined in the access means tobe the target resource.

In accordance with still another aspect of the present invention, thereis provided a computer-readable storage medium storing code for causinga processor-controlled finder server having access to: (a) a databaseincluding (i) an index of resources available on the network; and (ii)information regarding user feedback gathered in previous executions ofthe finder server by a user and plural previous users; and (b) alearning system structured to access and learn from informationcontained on the database, to perform a method of finding, in responseto entry by a user of a resource identity signifier, a single intendedtarget resource intended by the user to uniquely correspond to theresource identity signifier, among a plurality of resources located on anetwork comprising a plurality of interconnected computers. The methodcomprises: receiving a resource identity signifier from the user; andaccessing the database to determine, based on the information in thedatabase, which, if any, of the indexed resources is likely to be theintended target resource. Preferably, the method further comprises thestep of: directing a computer of the user so as to cause that computerto connect the user to the address of the resource, if any, determinedas likely to be the intended target resource.

In accordance with another aspect of the present invention, there isprovided a system for finding resources on a network of interconnectedcomputers on which a plurality of resources reside. The systemcomprises: a client terminal operated by a user, the client terminalallowing the user to connect to resources located on the network; and afinder server having access to: (a) a database including: (i) an indexof resources available on the network; and (ii) information regardinguser feedback gathered in previous operations of the system by the userand plural previous users; and (b) a learning system operable to accessand learn from information contained in the database. The finder serveris operable to locate, in response to entry by the user of a resourceidentity signifier, a single intended target resource intended by theuser to uniquely correspond to the resource identity signifier, fromamong a plurality of resources located on the network, by: receiving aresource identity signifier from the user; accessing the database todetermine, based on the information in the database, which, if any, ofthe indexed resources is likely to be the intended target resource; anddirecting a computer of the user so as to cause that computer to connectthe user to the address of the resource, if any, determined as likely tobe the intended target resource.

In accordance with another aspect of the present invention, there isprovided a method of identifying, in response to entry by a user of anobject identity signifier, a single intended object to be acted upon,the single intended object being intended by the user to uniquelycorrespond to the object identity signifier, among a plurality ofpossible objects. The method is for use on a computer having access to:(a) a database including (i) an index of possible objects; and (ii)information regarding user feedback gathered in previous executions ofthe method by the user and plural previous users; and (b) a learningsystem structured to access and learn from information contained in thedatabase. The method comprising: receiving an object identity signifierfrom the user; and accessing the database to determine, based upon theinformation in the database, which, if any, of the indexed objects islikely to be the object intended to be acted upon.

In accordance with another aspect of the present invention, there isprovided an apparatus for identifying, in response to entry by a user ofan object identity signifier, a single intended object to be acted upon,the single intended object being intended by the user to uniquelycorrespond to the object identity signifier, among a plurality ofpossible objects. The apparatus comprises: a computer having access to:(a) a database including (i) an index of possible objects; and (ii)information regarding user feedback gathered in previous executions ofthe method by the user and plural previous users; and (b) a learningsystem structured to access and learn from information contained in thedatabase, the apparatus being operable to: receive an object identitysignifier from the user; and access the database to determine, basedupon the information in the database, which, if any, of the indexedobjects is likely to be the object intended to be acted upon.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is an architectural block diagram of a server computer systeminternetworked through the Internet in accordance with a preferredembodiment of the present invention;

FIG. 1B is a flow diagram illustrating a method of obtaining feedbackfrom multiple users to be applied in searching or signifier mapping;

FIG. 2 is flow diagram showing a method of signifier mapping usingfeedback and heuristics to continually improve the performance of themapping;

FIG. 3 shows an example of a database entry for the finder server of thepresent invention;

FIG. 4A is a flow diagram illustrating a technique of feedback weightingfor probable results in signifier mapping;

FIG. 4B is a flow diagram illustrating a technique of feedback weightingfor possible results in signifier mapping; and

FIG. 5 is a flow diagram illustrating how feedback is used in apreferred embodiment to discriminate a probable target resource inaccordance with the present invention.

DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS

“Population Cybernetics” and the Internet

As a general matter, the present invention relates to a technique thatcollects experience (a knowledge base) from a mass population that isopen ended or universal, either over all domains, or over some definablesubject or interest domain or strata. This represents a significantimprovement over prior art techniques, which are generally limited inthe scope of the population and extent of experience from which theydraw their knowledge base.

The technique of the present invention, in a preferred embodiment, usesthe Internet to do this in a way that is powerful, economical, andfar-reaching. The technique, in the preferred embodiment, uses theInternet to enable collection and maintenance of a far more completeknowledge base than has been used with any prior technique exceptCollaborative Filtering (CF).

In the present invention feedback learning is advantageously utilized,so that the information is not just collected, but refined based onfeedback on the accuracy of prior inferences.

In its broad sense the present invention constitutes a kind of“population cybernetics,” in that the learning does not just collect alinear knowledge base, but uses a feedback loop control process toamplify and converge it based on the results of prior inferences, andthat it works over an entire population that is open, infinite, andinclusive. This is in contrast to prior learning techniques, which drawon necessarily finite, closed populations.

The use of population group information to achieve signifier mappingdiffers from the prior art technique of collaborative filtering in atleast the following manner:

Whereas both CF and the technique of the present invention draw onknowledge of a population group to make inferences, CF obtains ratingsof many things by many people to suggest other things (that may also behighly rated by the user, based on correlation with the group), and CFdoes not involve a specific input request, but rather seeks a new,previously unknown item in a category. On the other hand, the presentinvention obtains translations of many signifiers by many people tosuggest the intended translation of a signifier and involves a specificinput request to be translated to identify a known intended target

Although the technique of signifier mapping will occasionally bereferred to loosely as searching, it is more accurately translation,because the target is intended and known, just not well specified. Thisdiffers from typical Web or document searching, which typically seeksunknown, new items.

The technique of the present invention also differs from naturallanguage (NL) translation or understanding, in that the input has nocontext as part of a body of discourse (a text). NL understandingtechniques on the other hand translate words as components of conceptsembedded in texts having a context of related ideas. Thus the cues ofcontext in a discourse are absent, and the translation must be donewithout any such cues, although knowledge of the user may provide auseful context of behavior, demographics, psychographics that has somevalue in inferring intent, and knowledge of the user's prior requestsmay provide additional useful context information. The task is to inferor predict intention, rather than to understand meaning, because thereis no basis to infer meaning in any conceptual sense. The input isdisjointed from any surrounding context, and if not seen before (fromthe user or others), there is little useful information on either itsmeaning or intention. The present invention seeks to infer intentionbased on limited data, primarily the input request, and draws on groupdata (of request translations) as its strength.

The task of the present invention has similarities with cryptanalysis,in that both the present invention and cryptanalysis use data aboutcommunications behavior from groups of communicators to make inferences.However the task differs in that

-   -   Cryptanalysis deals with intentional hiding of meaning or        intention, where the technique of the present invention is        applied to cases where the hiding (of the intention of a        signifier) is not at all intended; and    -   Cryptanalysis seeks to infer meaning (ideas) drawing on context        in a discourse, like NL understanding, not usually to infer the        intention of a signifier (of objects or actions) which is not in        a context.

This point of intention versus meaning is subtle, but has to do withcommunication of commands or requests as opposed to concepts.

-   -   One view of this is the idea of “requests,” as opposed to        declarations or assertions, in the use of language.    -   This task of recognizing commands (vs. meanings) has parallels        in the task of robot control, such as that based on spoken        commands. The similarity is in training understanding of the        speech of many users to be speaker independent, and to infer        meanings of a current speaker from that of others. The        difference is that the tasks addressed in the present invention        deal with a very wide, effectively infinite universe of commands        (intended objects), while robot control techniques have        generally been limited to very small sets of commands (partly        because of the inability to apply mass experience).

Thus the technique of the present invention could be viewed asaddressing a special class of robot control (in which experience dataand feedback is accessible), and may ultimately be extensible to otherrobot control applications as such data becomes accessible over thenetwork.

The social dimension is critical for inferences relating to sharedobjects or resources. Names draw on social conventions and shared usage.This social usage information is essential to effective mapping ofsignifiers to resources. De-jure naming systems can underlie a namingsystem, as for current Internet domain names, but de-facto usage is theessential observable source of information for fullest use. De-juresystems suffer from entropy, corruption and substitution, while de-factousage is pragmatic and convergent to changing usage patterns.

This applies to a variety of name-able resources:

-   -   Web domain names;    -   Web sub-site names (such as to find sub-areas);    -   People or business names;    -   Department, agent, or service identifiers (such as to to find        contact points);    -   Policy capability specifications (such as to find permissions,        such as someone who can provide access to a given resource for a        given purpose, such as confirming employment status or        update-access to a report);    -   Information sets or collections (to find reference tools that        are known to exist, such as an IBM dictionary of acronyms, or an        index of papers in ACM publications);    -   Other robot control tasks, as social experience and feedback        becomes accessible.

Social usage information can be combined with other sources ofinformation in a heuristic fashion. For example, there could be ahierarchy that might be used in order, as available:

-   1. Personal defined usage information, such a defined personal    nicknames;-   2. Public de-jure defined mappings or directories;-   3. Personal usage information (a person's own undefined nicknames,    learned from that person's own usage/feedback);-   4. Social de-facto usage information;

This is just one possible sequence, but shows how the usage data cantake searching beyond what has been defined.

As discussed above, a preferred embodiment of the present inventionrelates to a method and apparatus for locating a desired target resourcelocated and accessible on a network, in response to user entry of aguessed name or alias. In illustrating the preferred embodiment, theapparatus is shown as a server computer, or computers, located as a nodeon the Internet. However, the present invention is in no way limited touse on the Internet and will be useful on any network having addressableresources. Even more broadly, the present invention is useful for anysimilar task of identifying an intended target for an action in whichautomatic facilitation of that action is desired, where feedback from alarge population can be obtained to learn whether a given response wasin fact the one that was desired. Control of robots, as discussedherein, is one example of such broader application.

The finder server of the preferred embodiment of the present inventionallows users to enter a guessed identifier or alias, as easily as ifthey knew the correct URL. Specifically, the finder server of thepresent invention accepts a guessed name, or alias, from a user, uses alook-up technique, enhanced by heuristics preferably taking into accountprevious users' actions, to determine a correct URL for the intendedtarget resource, and causes the user's browser to go to that URLautomatically. Preferably this is done without the added step of firstviewing and clicking on a search-results page, where an initial searchfinds the intended target resource with a predetermined degree ofcertainty. Such a resource will be referred to hereinafter as a“probable”. In accordance with a preferred embodiment of the presentinvention, this functionality can be implemented by:

-   -   Publicizing the locator server under an appropriate URL name,        for example, guessfinder.com.    -   Setting up the server to, in response to entry of a guessed name        or alias, do a lookup to the correct URL and return a response        that causes the user's browser to go automatically to the        specified URL. Such an automatic transfer can be effected using        a standard HTML facilities, such as a redirect page, or framing.    -   If the guess does not provide an exact match in the lookup        phase, using feedback and heuristic techniques to create and        present to the user a selection of links to possible matches.        Alternately, the user may be presented with a nomatch page with        advice, or directed to a conventional search interface, or        further directories.

It is contemplated that the use of aliases for attempting to locate aweb site associated with company name or brand name would be founduseful. For example, the aliases “s&p”, “s-p”, “sandp”, “snp”,“standardandpoors”, “standardnpoors”, “standardpoors” should preferablyall map to www.standardpoors.com. In addition to companies and brands,other important name domains would include publications, music groups,sports teams, and TV shows.

The present invention advantageously provides for learning and feedbackon the basis of user preferences to automatically and dynamically builda directory of names and sites that maps to the actual expectations andintentions of a large population of users, and adapts to changes overtime, including the appearance of new sites, thus optimizing utility tothem.

The finder server of the present invention effectively provides asecondary name space, administered by the organization operating thefinder system, through the automated heuristic methods described here,that maps to, but is not dependent on, the URL name space. The findersite computer has access to a data base containing entries for anynumber of popular sites, with any number of likely guesses andvariations for each site.

As a result of the service provided when the present invention isimplemented, site sponsors could skip the cumbersome and costly processof obtaining specific mnemonic URLs or alternate URLs in many cases(especially with regard to domain names). Even with a number ofconventional URLS, this service could be a supplement, for additionalvariations. The problem of pre-empted URL domain names would also beavoided, except where there is legitimate and significant pre-existingusage.

A key to utility is to be able to directly connect in response to mostguesses, and ambiguities could be a limiting factor. To avoid that it isdesirable to exploit Pareto's Law/the 80-20 rule and do a direct connecteven when there is an uncertain but likely target. For that to beuseful, it must be easy for users to deal with false positives.

Correction after arrival at a wrong site can be made relatively painlessby allowing a subsequent request to indicate an error in a way that tiesto the prior request and adds information. For example a request,guessfinder.com/lionking, that located the movie but was meant to findthe play could be corrected by entering guessfinder.com/lionking/play. Amore efficient coding might explicitly indicate an error, such asguessfinder.com/!/lionking/play. Even with the error, this would bequicker and easier than conventional methods. Note that this example wasillustrated with the direct URL coding techniques described below.Similar post-arrival corrections can be made with other user interfacetechniques, such as a frame header that includes appropriate userinterface controls to report feedback, much as conventional searchengines allow for “refinement” of prior searches, also described below.

Correction in-flight can be achieved by using the existing visibility ofthe redirect page, or enhancing it. When a redirect page is received bya user's browser, it appears for a short time (as specified with an HTMLrefresh parameter) while the target page is being obtained. In additionto affording a way to optionally present revenue-generating(interstitial) advertising content, that page preferably lists theredirection target, as well as alternatives, allowing the user to seethe resolution in time to interrupt it.

This is most useful with a browser that permits a redirect to be stoppedin mid-stream by clicking the stop button, leaving the redirect page ondisplay, and allowing a correct selection among alternative links to bemade. Alternately, a multi-frame (multi-pane) display could be used toallow a control frame to remain visible while the target page is loadingin a results frame, as described below.

Note some of the typical parameters and control points that would berelevant:

“New” Sites.

Applies when the user wants a site but is provided neither a direct hit,nor a correct possible. Users would find the site via alternate means(offered through the service or not). The user then submits an add-siterequest, via the Web or e-mail. If the number of add-site requests overa set interval exceeds a set (low) threshold, the site is added as apossible, or a direct hit if there are no competing alternatives. Suchadds would be provisional, and could be dropped if requests are notsustained.

Possibles

Low confidence possibles would be listed low on the list, and selectionswould be tracked. If selections are strong, they move up the list. Ifselections are very weak, they would drop off after some interval. Thethreshold to add back sites that were dropped might be higher for atime, to limit oscillation and false adds. If possibles are well aheadof alternatives by some threshold over some interval, they would bepromoted to direct hits.

Direct Hits

Feedback on false positives would be collected. This could be via linksin frames, redirect pages, interstitials, or other means, as suggestedpreviously. If false positives exceed a threshold, the site would revertto a possible and the common alternatives would be listed as well.

Parameter issues: thresholds, intervals, smoothing, damping, overrides.

Basic parameters include the various thresholds and time intervals formeasurement. Smoothing techniques (such as exponential smoothing) wouldbe applied to adjust for random variations and spikes, to improveforecasting. Damping mechanisms could be used to limit undue oscillationfrom state to state. Overrides would provide for mandated or prioritymatches, such as for registered trademarks, on either a weighted orabsolute basis, as appropriate.

FIG. 1A illustrates a first embodiment of the present invention, asimplemented on the Internet. The finder server 10 includes a computer orcomputers that perform processing, communication, and data storage toimplement the finder service. Finder server 10 includes a finderprocessing/learning module 101. Module 101 performs various processingfunctions, and includes a communication interface to transmit andreceive to and from the Internet 12, as well as with database 102, andis programmed to be operable to learn from experiential feedback data byexecuting heuristic algorithms. Database 102 stores, in a preferredembodiment, indexes of URL data that would allow the module 101 tolocate, with a high degree of confidence, a URL on the Web that is anexact match for a target resource in response to a user's entry of analias or guessed name. Preferably, the indexes store, in addition toavailable URL information, such as domain name directories, informationrelating to the experience of the server in previous executions of thefinder service. As the server gains experience and user feedback,heuristic techniques are applied by module 101 to enable the returnedURLs to conform more and more accurately to user expectations.

Users 11 ₀-11 _(N) can access the Internet 12 by means of clientcomputers (not shown) either directly or through an Internet serviceprovider (ISP). As has been discussed previously, to make use of thepresent invention the user enters a guessed name, or alias, into hiscomputer's browser and submits a query containing the alias to thefinder server. The World Wide Web 14 includes computers supporting HTTPprotocol connected to the Internet, each computer having associatedtherewith one or more URLs, each of which forming the address of atarget resource. Other Internet information sources, including FTP,Gopher and other static information sources are not shown in the figure.

The finder server includes operating system servers for externalcommunications with the Internet and with resources accessible over theInternet. Although the present invention is particularly useful inmapping to Internet resources, as was discussed above, the method andapparatus of the present invention can be utilized with any networkhaving distributed resources.

Entry of the alias by a user may be accomplished in a number of ways. Inone embodiment, a usage convention can be publicized for passing thealias to the server within a URL string, such asguessfinder.com/get?ibm, for example, for trying to find the web pagecorresponding to the alias “ibm”. In this case, the server is programmedto treat the string “ibm” as a search argument and perform theappropriate processing to map the alias to the intended target resource.A similar effect can be obtained by the somewhat simpler formguessfinder.com/ibm, if the server is programmed appropriately.Alternately, the user can visit the web site of the finder server and bepresented with a search form, as is done in conventional search engines.A third option is to provide a browser plug in that allows direct entryof the key word in the browser's URL window or any alternative localuser interface control that will then pass the entry on as a suitablyformatted HTTP request.

It also would be preferable for an enhanced user interface to be phasedin as the service gains popularity. This preferably would beaccomplished by a browser plug-in, or modifications to the browseritself, to allow the alias to be typed into the URL entry box withoutneed for the service domain name prefix (such as, guessfinder.com/ . . .). Instead, such an entry would be recognized as a alias, not a URL, andthe prefix would be appended automatically, just as http:// . . . isappended if not entered with a URL in current browsers.

FIG. 1B is a flow diagram illustrating a technique for obtaining andlearning from feedback responses gathered from a large group of people,in the example, users 1, 2, . . . n. Such a technique can be used in avariety of applications, and in particular in traditional searchengines, or in mapping to identify particular web sites, as in alias orsignifier mapping.

In FIG. 1B, users 1, 2, . . . n represent a large community of users. Inthe flow diagram, the flow of query items from the users is indicated bya Q, the flow of responses back to the users is indicated by an R, andthe flow of feedback results provided by the users' actions, orresponses to inquiries, is indicated by an F. As can be seen from thefigure, Query (a, 1) is transmitted from user 1 to the service 2, whichcan either be a searching or a mapping service. The service has learningprocessor 4, which interfaces with a database 6. The database 6contains, among other things, indexes and feedback information gatheredfrom previous queries. In response to the query, the user 1 is providedwith a response R(a, 1). User 1 then is provided with the opportunity totransmit user Feedback (a, 1) to the Service 2. Learning processor 4stores the feedback information in the database 6, and is programmedwith one or more heuristic algorithms enabling it to learn from thefeedback information to improve the returned search or mapping results.The feedback provided will improve the results offered, for example bypositively weighting results preferred by users, so that, over time,more accurate results can be obtained.

FIG. 2 is a diagram illustrating the logical flow used in applying thegeneral technique of learning from user feedback shown in FIG. 1 tosignifier mapping, in accordance with a preferred embodiment of thepresent invention. A user enters a Query consisting of a signifier,represented by Q^(s). The server, in response to receipt of the query,parses the query, at step S02, and in step S04 performs a databaselookup in an attempt to determine, if possible, the exact targetresource intended by the user. Database 6 includes index data as well asfeedback data obtained from users in previous iterations of thesignifier mapping program, is accessed. The stored data structure isdescribed in more detail below.

In step S06, the program discriminates a probable intended target makinguse of the index information such as domain registration indexes, andother resources, as well as the feedback information stored in thedatabase. In step S08, if a likely hit, or exact match has beenidentified, that is, a web page has been located with a high confidenceparameter, the flow continues to step S10. At step S10, a direction isprepared to the likely hit URL. A list of alternatives optionally may beprovided for presentation to the user at the same time, in case thelikely hit turns out not to be the target identifier. At step S12, theserver sends information R^(s) to the user, more particularly to theuser's browser, to effect a link to the likely hit. Optionally, thealternate list is also provided at the same time.

In step S14, the viewed page is monitored by the server and the user, byhis actions, provides feedback. Most readily determined with noassistance from the user is the fact of the user having chosen the link.This may be determined, for example by a redirect, in which anintermediate server is transparently interposed between the browser andthe target page, and thus able to identify the user and the URL targetbased on coding built into the URL that the user clicks. Also desirableis the amount of time the user spends at the site, which will be anindicator of whether the site is the intended target. This may beascertained, for example, if clickstream data can be obtained, such asthrough the use of a monitor program that works as a browser add-in orWeb accessory, such as the techniques offered by Alexa. Other feedbackcan be provided by asking the user. This can, for example, be doneconveniently by using a small header frame served by the relocationservice that appears above the actual target page, and that includescontrols for the user to indicate whether or not the results werecorrect. The URL of the viewed page is recorded, together with any otherfeedback, for use in improving the accuracy of subsequent iterations ofsignifier mapping. At step S26, the feedback data is supplied to afeedback weighting algorithm, described in detail below, which generatesappropriate weighting factors to be stored in the database for use insubsequent mappings.

If it is determined at step S08 that the result is not a likely hit, theflow proceeds to step S18, where a list of the top m hits (m being apredetermined cutoff number), preferably drawing on the list of possiblehits from a conventional search engine, or by employing the sametechniques as a conventional search engine, is prepared. Unlikeconventional search engines, the ranking of these hits is basedprimarily on experience feedback data as described below. In addition,where such feedback is limited or absent, it would be supplemented byvariants of more conventional search engine weighting rules that areexpressly tuned to the task of finding a single intended result (i.e.,high relevance by low recall) rather than many results (high relevanceplus high recall). The list is presented, at step S20, to the user asR^(s). The user, by the selections made from the provided list, and fromother feedback, such as how long the user spends at each link, suppliesfeedback to the system. This information F^(s) is monitored, at step S22and recorded, at step S24. The recorded information is supplied to thefeedback weighting algorithm, at step S26, the output of which is storedin the database for use in subsequent iterations of the signifiermapping.

FIG. 2 illustrates the simple case in which a user is directed to atarget URL if the target has been determined to be a probable hit, andis presented with a list to choose from if the target cannot beidentified with sufficient certainty. However, it is well within theintended scope of the invention for alternate methods to be employed.For example, the user interface (UI) could be extended, either byframing, or a browser plug-in or extension, to providemulti-pane/multi-window results that allow a pane for each type ofresponse, e.g., the target response and a list of possibles, regardlessof the level of confidence in the result. In such a case, the format forpresentation of results would be the same whether a probable has beenlocated or not, but the learning from feedback and ranking would stillseek to determines “correctness” based on the varying feedback cases.

FIG. 3 illustrates a preferred method of organizing index data to allowfor storing and updating of the most probable hits for a given query. Ascan be seen from the illustration, for each query, whether singleelement queries or compound queries, there is stored a list ofassociated possible targets. Linked to each of these query/target pairsis a raw score, an experience level, and a probability factor. Asfeedback enters the system, the index data is updated to reflect theuser feedback. The updating process will be described below. While theindex shows preferred weighting criteria, these are only a sample of thekind of criteria that can be correlated to the query/target pairs. In asimple embodiment, the raw score would be based only on selections ofhits, and explicit feedback on correctness as described below. Otherembodiments could add feedback data on time spent at a target.Additional variations would include weighting based on the recency ofthe feedback, and on the inclusion of non-feedback data, such as thevarious syntactic and semantic criteria used for relevance weighting byconventional search engines.

The process of maintaining the guess-target database is adaptable to ahigh degree of automation, and this can be highly responsive to newsites. An outline of such a method is:

All guesses are logged and analyzed.

Ambiguous hits are tracked as described earlier.

Complete non-matches are sorted by frequency to identify common newrequests (in real time). Changes in ambiguous match patterns could alsoflag appearance of new sites.

Common new requests preferably are fed to an automated search tool thatwould use existing search engines, hot site lists, and name registrationservers, etc. to identify possible targets.

Automated intelligent analysis of those results can seek to qualifyprobable targets.

High confidence (or possible) targets preferably are added, and thentracked based on the feedback mechanism described earlier, in order toself-correct. A confidence parameter preferably is used to controlwhether to redirect or to present a possibles list to users.

Human review and correction also preferably is used to supplement this.

FIG. 4A illustrates a preferred technique for weighting the resultsusing feedback data for hits that have been determined to be probablehits. In step S30, if the user feedback from the probable resultindicates that the probable was in fact the target URL the user wassearching for, the flow proceeds to step S32 where the raw score forthat query/target pair is incremented by factor_(Y). If the user returnsfeedback indicating that the probable was not the target resource theuser had in mind, the flow proceeds to step S34 where the raw score forthat query/target pair is decremented by factor_(N). If the userprovides no feedback, then the flow proceeds to step S36 where the rawscore is decremented by factor_(O), which can be zero. After executionof any of steps S32, S34 or S36, the flow proceeds to step S38, at whichthe experience level score is incremented by Efactor_(C).

FIG. 4B illustrates a preferred technique for weighting in accordancewith user feedback in the case of possibles, i.e., items on the listpresented to the user when no probable result can be located. As shownin the figure, if a possible is selected by the user from the presentedlist, at step S40, the fact of selection is recognized, preferably byuse of a redirect server that allows the system to keep track of whichlink was chosen. Additionally, the amount of time the user spends at theselected link may be ascertained. Making use of the information gatheredin the redirect and such other feedback as may be obtained, the rawscore for the query/target pair is incremented, at step S44, byfactor_(S). The user is then requested to provide additional feedbackafter the user has finished viewing the link.

In a preferred embodiment of the present invention, this feedback isgathered from the user by presenting the user with a frame that includesa mechanism, such as a check box, or radio button, that allows the userto indicate whether the selected possible was in fact the intended or“correct” target resource. If it is determined, at step S42, from thefeedback that the link was the correct target, the flow proceeds to stepS46, where the raw score for that query/target pair is incremented byfactory_(Y′). If the user returns a negative response, the raw score ofthe pair is decremented at step S48 by a by factor_(N′). If no feedbackis received, the raw score is decremented, at step S50, by factor_(O′),which can be zero. After execution of any of steps S44, S46, S48 or S50,the flow proceeds to step S52, at which the experience level score isincremented by Efactor_(PS) in the case of selection of the link, and byEfactor_(PC) if the link was the correct.

FIG. 5 illustrates a detail of how the present invention ranks anddiscriminates a probable target. At step S100 a list of possibles isobtained. Next, the list is ranked, at step S102, on the basis of theexpected probability as the target. In step S104, a discriminationcriteria is calculated and compared with a predetermined thresholdparameter. For example, if ProbTi is the expected probability that Ti isthe correct target, a formula such as the example shown can be used todetermine whether T1 stands out as more probable than T2 by a relativemargin that exceeds a set threshold needed to judge it as the probableintended one target. When the threshold is not exceeded, the implicationis that one of the secondary possibilities may very well be the intendedone, and that directing the user to the slightly favored target may notbe desirable.

In the preferred embodiment, when a link on a list of possibles isselected by the user, rather than connect the user immediately to thechosen link, the finder server first redirects the user to a redirectserver where feedback data relating to the selection can be gathered.One item of feedback that may be obtained in this manner is the veryfact of the selection. Further feedback can be obtained by additionalmeans, such as monitoring how long the user spends at the selected link,and by directly querying the user.

The redirect linking technique uses the target URL as a server parameterwithin a composite URL to control the intermediate server parameterwithin the URL to control the intermediate server. The target URL isembedded as a server parameter within a URL that addresses the redirectserver, and the URL parameter is used to control the intermediate serverprocess. Thus a server is called with a first URL, a redirect URL, thatspecifies the second URL, i.e., the target URL, as a parameter. Forexample

http://redirector.com/redirector?query12345678/targetserver.com/targetpath1/targetpage1.htm

where redirector.com is the intermediate server URL, query12345678 is aunique identifier of the user-query combination, andtargetserver.com/targetpath1/targetpage1.htm is the target URL. Thenetwork ignores the parameter portion of the URL, which is passed asdata to the server. The server acts on the parameter to perform desiredintermediary processing, in this case, the logging of the fact that thislink was clicked in response to query12345678, and to redirect the userto the intended location specified by the second URL. The tokenquery12345678 could be a unique identifier corresponding to a loggeduser-query entry, or it could be the actual query string.

The delay required for the redirect provides the opportunity for thedisplay of interstitial advertisements. In addition, additional userfeedback can be solicited during the delay, and the connection to thetargeted URL can be aborted if the user indicates that the target siteis not the one he or she intended. In addition to using the redirectwhen a link is selected, the technique also preferably is used when anexact match is found, to provide a brief delay before connecting theuser to the exact match, to present advertisements to give the user thetime to abort the connection. In any event, the user preferably is giventhe opportunity to provide feedback after connecting to any site,whether directly as a result of an exact match, or as a result ofselecting from a linked possibles list.

The redirect server of the present invention allows data to be gatheredon each link as it is followed and redirected. The redirect link can becreated in a simple static HTML. However, it is preferable to create thelink dynamically for each user selection.

The finder is setup to recognize the feedback function, possibly as aCGI or other gateway/API function, and invoke the appropriate functionto parse the URL or other data (referer, cookies, etc.), extract thetarget URL and feedback information for processing, and return a pagecontaining a redirect (or use framing or other means) to take the userto the desired target.

This mechanism is general, and can be used for many purposes. In thecase of the finder server:

-   -   Reasonably complex feedback information can be obtained, which        at minimum would include the original guess. Thus a log of each        guess that was not clearly resolved, paired with the        corresponding user-selected target, can be obtained.    -   That set of selected guess/target pairs can then be used to        adjust the confidence levels in the guess/target database.        Similar data on directly resolved pairs would also be applied,        along with any data from wrong-match reports.

Other applications are to any situation where links go to sites otherthan the source. This would include results of conventional searchengines, as well as resource directories, sites referring users tosuppliers, advertisers, etc.

It should be noted that the term server used throughout is not limitedto a single centralized hardware unit. The server functionalitydescribed herein may be implemented by plural units utilizingdistributed processing techniques well known in the art, and may beconnected by any conventional methods, such as on a local area network(LAN) or a wide area network (WAN).

While the present invention has been discussed primarily in terms of itsapplicability to searching the Web, the concept has much broaderapplicability. For example, in the area of robot control, the abovetechniques can be used to allow a robot to understand more readily theactual intent of a command.

For example, in the general case, analogous to discovery searching, therobot command may be performable in many ways, such as “direct theexcess inventory out of the active holding bin,” allowing the robot tofind any of several allowed places to move the inventory to, and leavingsome degree of ambiguity that complicates translation. In the n=1 case,or signifier mapping, more specific feedback heuristics can be utilizedas described above for Web signifier searches, to assist the robot indetermining the one acceptable action to be taken in response to thecommand such as “direct the excess inventory to the secondary holdingbin.”

Another example is a plant-floor robot that responds to natural-languagetyped or voice commands that could be told “shift the connection fromthe output rack from chute number 1 to chute number 2.” This techniquewould be highly useful in highly replicated plants, such as localrouting centers for a national package express network.

Yet another example would be a smart TV that is responsive to voice ortyped commands that is told “turn on the Giants football game.” Such adevice could be linked to a central server to aid in learning to relatecommands and details of current programming. The process is almostexactly as outlined for Internet searching above. Another example is apost office mail sorter that identifies zip codes as commands forrouting, based, for example, on OCR techniques or voice activation. Inthis case the queries would be the patterns in the optical scanner orthe voice digitizer, and the correctness of hits would be tracked in anyof various ways. The same process of the present invention would enablelearning that would enhance the level of recognition and correct mappingto intended zip codes.

The above embodiments of the present invention have been described forpurposes of illustrating how the invention may be made and used. Theexamples are relatively simple illustrations of the general nature ofthe many possible algorithms for applying feedback data that arepossible. However, it should be understood that the present invention isnot limited to the illustrated embodiments and that other variations andmodifications of the invention and its various aspects will becomeapparent, after having read this disclosure, to those skilled in theart, all such variations and modifications being contemplated as fallingwithin the scope of the invention, which is defined by the appendedclaims.

1-31. (canceled)
 32. A method of finding a target resource in responseto a user input, the method comprising: recognizing the user input as aresource signifier, wherein the resource signifier is independent ofregistered elements contained in a resource locator, the resourcelocator identifying at least one of a plurality of resources; accessingdatabase information that includes an index of available resources on anetwork of interconnected computers, wherein some of the plurality ofresources are identified by resource locators containing registeredelements; learning a social usage of the resource signifier frommulti-user feedback gathered from a plurality of users based on previoussocial usage of the resource signifiers, wherein users are enabled tosuggest associations between the resource signifiers and at least one ofthe plurality of resources that is to be included as part of the socialusage; and determining which, if any, of the indexed resources is likelyto be a target resource that corresponds to the recognized resourcesignifier based on the social usage of the recognized resource signifierwithout regard to identification in the recognized resource signifier ofregistered elements in any resource locator corresponding to theresource.
 33. The method according to claim 32, wherein a resource isdetermined as likely to be a target resource if the database informationindicates that a confidence level associated with that resource is of atleast a predetermined level.
 34. The method according to claim 33,further comprising: determining if none of the indexed resources has anassociated confidence level of at least the predetermined level; andpresenting the user with a list of one or more links to possibleresources, the list being ordered according to confidence level, with aresource having a highest confidence level being ranked highest.
 35. Themethod according to claim 32, further comprising; presenting the userwith a list of one or more links to possible resources, the list beingordered according to confidence level, with a resource having a highestconfidence level being ranked highest.
 36. The method according to claim33, wherein the confidence level is determined predominantly based onpopularity of social usage.
 37. The method according to claim 32,wherein users may elect to have the method performed based especially ontheir personal usage, learned from their personal entries and/orfeedback, with reduced or no regard to the usage of other users.
 38. Themethod according to claim 32, wherein users may elect to have the methodperformed based on their personal usage, learned from their personalentries and/or feedback, with no regard to the usage of other users. 39.An apparatus configured to perform the method of claim
 32. 40. Acomputer readable medium comprising computer executable instructions forperforming the method of claim
 32. 41. A method of finding a targetresource in response to a user input, the method comprising:recognizing, from the user input, one or more resource signifiers;determining associations between the resource signifiers and resourcelocators containing registered elements, said resource locatorsidentifying resources available on a network, wherein the determining isbased on previous social usage of the resource signifiers by a pluralityof users wherein users are enabled to suggest associations between theresource signifiers and at least one of the plurality of resources thatis to be included as part of the social usage, and wherein the resourcesignifiers are independent of the registered elements; and determining atarget resource of the user from the resources available on the networkbased on the user input and the social usage of the resource signifiersrecognized in the user input, without regard to matching the resourcesignifiers to any registered element.
 42. The method according to claim41, wherein determining the target resource comprises determiningwhether a confidence level for the target resource is of at least apredetermined level.
 43. The method according to claim 41, whereindetermining associations between the resource signifiers and resourcelocators containing registered elements identifying resources availableon the network comprises: presenting the user with an ordered list oflinks to possible resources on the network, the list being orderedaccording to confidence level; and determining one or more selections bythe user of links in the ordered list.
 44. The method according to claim41, further comprising presenting the user with a list of one or morelinks to possible resources, the list being organized based on an extentto which the social usage of the resource signifiers signify thepossible resources.
 45. The method according to claim 44, wherein thelist is organized based on popularity of social usage.
 46. The methodaccording to claim 41, wherein users may elect to have their socialusage of resource signifiers learned from their personal usage of theresource signifiers, without regard to the social usage of other users.47. The method according to claim 41, wherein users may elect to havethe target resource determined using associations between the resourcesignifiers and resource locators containing registered elementsidentifying resources that are based predominantly on their personalusage of the resource signifiers.
 48. A method of locating a targetresource on the Internet, the method comprising: receiving, from a user,a user input including at least one expressions; determining anassociations between the at least one expressions and one or more URLsassociated with candidate resources, the determining being based, atleast in part, on a previous social usage of the at least oneexpression; enabling the user to suggest an association between the atleast one expression and one or more URLs associated with candidateresources; and determining which of the URLs associated with thecandidate resources is likely to be the URL associated with the targetresource based on the at least one expression and the previous socialusage of the at least one expression, without regard to matching anyportion of any text in the at least one expressions to any text in theURL associated with the target resource.
 49. The method according toclaim 48, wherein the method is performed at least in part by a serveron the Internet.
 50. The method according to claim 48, wherein thereceiving, from a user, a user input is done via an interaction with aWeb browser.
 51. The method according to claim 48, wherein one of thelikely intended target resource URL and the likely intended targetresource, itself, is sent to the user to be presented to the user via aWeb browser.
 52. The method according to claim 48, wherein the user isnot an editor having special editorial privileges granted by the entityresponsible for the operation of the method.
 53. The method according toclaim 48, wherein the expression received from the user is recognized tobe a resource signifier.